Table Of ContentEmergence, Complexity and Computation ECC
Paul Rosin
Andrew Adamatzky
Xianfang Sun Editors
Cellular Automata
in Image Processing
and Geometry
Emergence, Complexity and Computation
Volume 10
Serieseditors
IvanZelinka,TechnicalUniversityofOstrava,Ostrava,CzechRepublic
e-mail:[email protected]
AndrewAdamatzky,UniversityoftheWestofEngland,Bristol,UnitedKingdom
e-mail:[email protected]
GuanrongChen,CityUniversityofHongKong,HongKong
e-mail:[email protected]
EditorialBoard
AjithAbraham,MirLabs,USA
AnaLuciaC.Bazzan,UniversidadeFederaldoRioGrandedoSul,PortoAlegre
RSBrasil
JuanC.Burguillo,UniversityofVigo,Spain
SergejCˇelikovský,AcademyofSciencesoftheCzechRepublic,CzechRepublic
MohammedChadli,UniversityofJulesVerne,France
EmilioCorchado,UniversityofSalamanca,Spain
DonaldDavendra,TechnicalUniversityofOstrava,CzechRepublic
AndrewIlachinski,CenterforNavalAnalyses,USA
JouniLampinen,UniversityofVaasa,Finland
MartinMiddendorf,UniversityofLeipzig,Germany
EdwardOtt,UniversityofMaryland,USA
LinqiangPan,HuazhongUniversityofScienceandTechnology,Wuhan,China
GheorghePa˘un,RomanianAcademy,Bucharest,Romania
HendrikRichter,HTWKLeipzigUniversityofAppliedSciences,Germany
JuanA.Rodriguez-Aguilar,IIIA-CSIC,Spain
OttoRössler,InstituteofPhysicalandTheoreticalChemistry,Tübingen,Germany
VaclavSnasel,TechnicalUniversityofOstrava,CzechRepublic
IvoVondrák,TechnicalUniversityofOstrava,CzechRepublic
HectorZenil,KarolinskaInstitute,Sweden
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AboutthisSeries
The Emergence,Complexityand Computation(ECC) series publishesnew devel-
opments,advancementsandselectedtopicsinthefieldsofcomplexity,computation
andemergence.Theseriesfocusesonallaspectsofreality-basedcomputationap-
proachesfromaninterdisciplinarypointofviewespeciallyfromappliedsciences,
biology, physics, or Chemistry. It presents new ideas and interdisciplinary insight
onthe mutualintersectionofsubareasofcomputation,complexityandemergence
anditsimpactandlimitstoanycomputingbasedonphysicallimits(thermodynamic
and quantumlimits, Bremermann’slimit, Seth Lloyd limits...) as well as algorith-
mic limits (Gödel’s proof and its impact on calculation, algorithmic complexity,
theChaitin’sOmeganumberandKolmogorovcomplexity,non-traditionalcalcula-
tionslike Turingmachineprocessand its consequences,...)and limitationsarising
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analogic computing,chaos computingand computingon the edge of chaos, com-
putationalaspectsofdynamicsofcomplexsystems(systemswithself-organization,
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asmodernmethodsofcomputation.Withinthescopeoftheseriesaremonographs,
lecture notes, selected contributionsfrom specialized conferencesand workshops,
specialcontributionfrominternationalexperts.
·
Paul Rosin Andrew Adamatzky
Xianfang Sun
Editors
Cellular Automata
in Image Processing
and Geometry
ABC
Editors
PaulRosin XianfangSun
SchoolofComputerScience&Informatics SchoolofComputerScience&Informatics
CardiffUniversity CardiffUniversity
Cardiff Cardiff
UnitedKingdom UnitedKingdom
AndrewAdamatzky
UnconventionalComputingCentre
UniversityoftheWestofEngland
Bristol
UnitedKingdom
ISSN2194-7287 ISSN2194-7295 (electronic)
ISBN978-3-319-06430-7 ISBN978-3-319-06431-4 (eBook)
DOI10.1007/978-3-319-06431-4
SpringerChamHeidelbergNewYorkDordrechtLondon
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(cid:2)c SpringerInternationalPublishingSwitzerland2014
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Preface
Imageprocessingandmathematicalmorphologyroutinesarenaturallyparallel.The
intensityofapixelisupdateddependingontheintensitiesofitsclosestneighbouring
pixels.Theintensitiesofpixelsareupdatedsimultaneously,indiscretetimeandby
the samerule.Thisisexactlyhowa cellularautomatonworksif theintensity ofa
pixelistakenasthestateofacell.Acellularautomatonisaregulararrayoflocally
connectedfinitestatemachines,orcells,whichtakediscretestatesandupdatetheir
statesinparallelindiscretetimedependingonthestatesoftheirclosestneighbours.
Thereforecellularautomatawouldbeafirstportofcallwhendesigningcomputing
paradigms,architecturesandimplementationsaimedatsolvingproblemsofimage
processing,patternrecognitionandgeneration.
This area of research is becoming increasingly active. Unfortunately, there are
few books, and no journals or conferences that focus on cellular automata based
approachestoimageprocessing,patternrecognitionandgeneration.Agoalofthis
bookistoprovideasinglesource,collectingtogethersuchwork.Eachchaptercon-
tainsabriefliteraturereviewoftherelatedwork.Thiswillprovemoreconvenient
thansearchingtheenormousresearchliteratureforrelevantpapers,whicharecur-
rentlywidely scattered.Moreover,this concentrationof materialwill also demon-
stratethebreadthandeffectivenessofcellularautomatabasedapproachestoimage
processing,patternrecognitionandgeneration.
We invitedexpertsin computerscience to share their findings,viewsand ideas
on what exact problems of image processing, pattern recognition and generation
canbeefficientlysolvedincellularautomataarchitectures,andtoprovidedetailed
descriptionsofexactcellularautomatonmachinestheyareusing.Thetopicscovered
include
• image compressionand resizing: “Cellular Automata forEfficientImage and
VideoCompression”byDogaruandDogaru,“CellularAutomataforImageRe-
sizing”byIoannidis,SirakoulisandAndreadis;
• skeletonization,erosionanddilation:“SkeletonizingDigitalImageswithCel-
lularAutomata”byDíaz-Pernil,Peña-CantillanaandGutiérrez-Naranjo,“Image
ProcessingAlgorithmsImplementationUsing QuantumCellular Automata”by
MardirisandChatzis;
VI Preface
• convex hull computation, edge detection and segmentation: “Convex Hulls
andMetricGabrielGraphs”byMaignanandGruau,“EdgeDetectionusingCel-
lularAutomata”byRosinandSun,“TheApplicationofCellularAutomatonin
MedicalSemiautomaticsegmentation”byYangandGao;
• forgery detection and content based retrieval: “Copy-move Forgery Detec-
tionUsing CellularAutomata”byTralíc, Rosin, SunandGrgic,“ActiveImage
ForgeryDetectionusingCellularAutomata”byTafti,Hassannia,“Content-based
ImageRetrievalWithCellularAutomata”byvanZijl;
• patterngeneration:“Cellular AutomatonShadingforBuildingEnvelopes”by
Zawidzki,“PatternFormationUsingCellularAutomataandL-Systems:ACase
Study in Producing Islamic Patterns” by Minoofam, Dehshibi, Bastanfard and
Shanbehzadeh, “Interactive Cellular Automata Systems for Creative Projects”
byForbes.
Weseethepotentialimpactofthebookintheadvancementofthetheoryofimage
processing,patternrecognitionandgenerationandthedesignofefficientalgorithms
andhardwareforparallelimageprocessingandanalysis.Thebookaimsatcomputer
scientists, softwareprogrammers,electronicengineers,mathematiciansandphysi-
cists, and at everyone who studies or develops cellular automaton algorithms and
toolsforimageprocessingandanalysis,ordevelopsnovelarchitecturesandimple-
mentations of massive parallel computing devices. The book will be an attractive
reading for a general audience because it has do-it-yourself appeal: all computer
experiments presented in the book can be implemented with minimal knowledge
ofprogramming.Simplicityyetsubstantialfunctionalityofthecellularautomaton
approachandtransparencyofthealgorithmsproposedmakesthebookanidealsup-
plementaryreadingin courseson image processing,parallelcomputing,automata
theoryandapplications.
Cardiff,UK PaulRosin
Bristol,UK AndrewAdamatzky
Cardiff,UK XianfangSun
March2014
Contents
1 CellularAutomataforEfficientImageandVideoCompression .... 1
RaduDogaru,IoanaDogaru
1.1 IntroductionandMotivation ............................... 1
1.2 AnAlternativetoCompressiveSensingBasedonCellular
AutomataScan .......................................... 5
1.2.1 PrincipleofChaoticScan ......................... 5
1.2.2 PropertiesoftheChaoticCounters .................. 7
1.2.3 DesigningGoodChaoticCountersasHybridCellular
Automata ....................................... 10
1.2.4 MessageRecoveryandExamples ................... 12
1.3 ImageCompressionBased onDictionariesGeneratedby
CellularAutomata(CA-VQ)............................... 15
1.3.1 The GeneralFramework of Dictionary Based
Compression .................................... 15
1.3.2 LearningCA-BasedDictionariesandPerformance
EvaluationoftheCA-VQSystem ................... 16
1.4 HardwareDescriptionandSynthesisofCAUsingAlgebraic
NormalForm............................................ 20
References.................................................... 21
2 CellularAutomataforImageResizing ......................... 25
KonstantinosIoannidis,GeorgiosCh.Sirakoulis,IoannisAndreadis
2.1 Introduction............................................. 25
2.2 CellularAutomataFundamentalsandCannyEdgeDetector..... 29
2.2.1 CAFundamentals ................................ 29
2.2.2 CannyEdgeDetector ............................. 30
2.3 ProposedMethod ........................................ 31
2.3.1 EdgeDetection .................................. 31
2.3.2 CAResizing..................................... 32
2.3.3 RemappingProcess............................... 33
2.4 ExperimentalResults ..................................... 34
2.5 HardwareImplementation................................. 40
VIII Contents
2.6 DiscussionandConclusions ............................... 42
References.................................................... 43
3 SkeletonizingDigitalImageswithCellularAutomata ............ 47
DanielDíaz-Pernil,FranciscoPeña-Cantillana,
MiguelA.Gutiérrez-Naranjo
3.1 Introduction............................................. 47
3.2 SkeletonizingAlgorithms ................................. 49
3.3 GuoandHallAlgorithm .................................. 51
3.4 CellularAutomata........................................ 53
3.5 ParallelImplementation................................... 55
3.5.1 Examples ....................................... 56
3.6 Conclusions............................................. 60
References.................................................... 60
4 ImageProcessingAlgorithmsImplementationUsingQuantum
CellularAutomata.......................................... 65
VassiliosMardiris,VassiliosChatzis
4.1 Introduction............................................. 65
4.2 MathematicalMorphology ................................ 67
4.3 Quantum-dotCellularAutomata ........................... 69
4.4 Methodology............................................ 73
4.4.1 Analysis ........................................ 73
4.4.2 CircuitDesign ................................... 73
4.4.3 Simulation ...................................... 75
4.5 QCAImplementationofMorphologicalOperations ........... 75
4.5.1 QCAImplementationofMorphologicalErosion....... 76
4.5.2 QCAImplementationofMorphologicalDilation ...... 77
4.6 Conclusions............................................. 80
References.................................................... 80
5 EdgeDetectionUsingCellularAutomata....................... 85
PaulL.Rosin,XianfangSun
5.1 Introduction............................................. 85
5.2 BoundaryDetection ...................................... 86
5.3 EdgeDetectioninIntensityImages ......................... 89
5.4 Post-ProcessingofEdges.................................. 92
5.4.1 ASimpleEdgeLinkingScheme .................... 93
5.5 Experiments............................................. 94
5.6 Conclusions............................................. 100
References.................................................... 101
6 Copy-MoveForgeryDetectionUsingCellularAutomata.......... 105
DijanaTralic,PaulL.Rosin,XianfangSun,SonjaGrgic
6.1 Introduction............................................. 106
6.2 Copy-MoveForgery(CMF)................................ 107
Contents IX
6.3 Copy-MoveForgeryDetection(CMFD) ..................... 107
6.3.1 Block-BasedMethodforCMFD .................... 109
6.3.2 PossibleFeatureVectors........................... 111
6.4 CAforCMFDetection.................................... 112
6.4.1 RepresentationofImageinBinary .................. 112
6.4.2 PlainCMF ...................................... 116
6.4.3 ApplicationonPost-processedImages ............... 119
6.5 FutureWork............................................. 122
6.6 Conclusion.............................................. 122
References.................................................... 123
7 ActiveImageForgeryDetectionUsingCellularAutomata ........ 127
AhmadPahlavanTafti,HamidHassannia
7.1 Introduction............................................. 127
7.2 Scenario1:UsingCellularAutomataandLUDecomposition ... 129
7.2.1 ProposedModel.................................. 130
7.3 Scenario2:UsingCellularAutomataandSingularValue
Decomposition .......................................... 134
7.3.1 ProposedModel.................................. 134
7.4 DatasetandExperimentalResults........................... 136
7.4.1 PerformanceandVisualQuality .................... 137
7.4.2 TimeConsumption ............................... 138
7.4.3 TrueandFalseAlert .............................. 138
7.4.4 PSNR .......................................... 139
7.4.5 SecretKeySensitivity............................. 139
7.4.6 Diffusion ....................................... 141
7.4.7 ComparisonwithNon-CAWorks ................... 142
7.5 Limitations ............................................. 142
7.6 ConclusionandFutureWork............................... 144
References.................................................... 145
8 Content-BasedImageRetrievalwithCellularAutomata.......... 147
LynettevanZijl
8.1 Introduction............................................. 147
8.2 Content-BasedImageRetrieval:ABackground ............... 147
8.3 CellularAutomatainImageProcessingandFeatureAnalysis ... 149
8.3.1 NoiseReduction ................................. 149
8.3.2 EdgeDetection .................................. 152
8.3.3 Segmentation.................................... 153
8.3.4 ColourMatchingandHistograms ................... 153
8.3.5 ShapeMatching.................................. 155
8.4 PracticalCaseStudy:RecognitionofLEGOBricks............ 156
8.5 Conclusion.............................................. 160
References.................................................... 160
